In this paper, a two-level optimization algorithm is developed based on rapid magnetic equivalent circuit (MEC) model and manifold mapping (MM) method. The performance of an axial flux permanent magnet machine (AFPM) ...
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ISBN:
(纸本)9798350348958;9798350348965
In this paper, a two-level optimization algorithm is developed based on rapid magnetic equivalent circuit (MEC) model and manifold mapping (MM) method. The performance of an axial flux permanent magnet machine (AFPM) is optimized using this approach. The rapid MEC model based on grid division exhibits significant computational efficiency, while reducing the influence of artificial factors in modeling process. Further, quantitative comparison of the error performance under different combinations of divisions are carried out. Subsequently, coarse and fine models are selected, respectively. The iterative process indicates that the modeling approach enables the coarse model to correctly follow the trends of fine model as input changes. As a result, the fast convergence of the MM is successfully achieved.
The aim of this work is to investigate a Wireless Power Transfer (WPT) system in different configurations from the perspective of human exposure to the electromagnetic field at close range. For this purpose, the guide...
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ISBN:
(纸本)9798350385236;9798350385243
The aim of this work is to investigate a Wireless Power Transfer (WPT) system in different configurations from the perspective of human exposure to the electromagnetic field at close range. For this purpose, the guidelines of the ICNIRP (International Commission on Non-Ionizing Radiation Protection) and the IEEE/ICES (International Committee on Electromagnetic Safety) were followed. Furthermore, the efficiency aspect is taken into account for the evaluation of the coupling factor for different scenarios. A 3D model was created and simulated by using FEM software. The results of the simulation provide information on the positioning and design of a suitable shielding with high transmission efficiency and offer a suggestion for potential material savings.
This paper presents an adaptive expectation-based improved genetic algorithm (AE-IGA) for the photovoltaic (PV)-energy storage system (ESS)-integrated distribution network, which helps to improve power quality (PQ) an...
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ISBN:
(纸本)9798350382570;9798350382563
This paper presents an adaptive expectation-based improved genetic algorithm (AE-IGA) for the photovoltaic (PV)-energy storage system (ESS)-integrated distribution network, which helps to improve power quality (PQ) and renewable energy sources consumption. Three optimization objectives are considered in the PV-ESS-integrated distribution network, including maximizing economic benefits, minimizing voltage deviation, and maximizing the power factor. To avoid the complicated decision mechanism, obvious subjectivity and poor applicability in the multi-objective optimization problem (MOPs), the IGA is firstly utilized to obtain the Pareto set (PS) of MOPs. Then, an AE method is designed for selecting the desired nondominated solution from the PS. Finally, simulation results demonstrate that the proposed scheme improves the PV penetration and reduces voltage deviation, and improves the power factor in the distribution network.
In today's fast-paced world, teachers seek continuous improvement for their courses to match their context using e-learning systems. With the aid of technologies, teachers have access to vast amount of resources w...
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ISBN:
(纸本)9798331516642;9798331516635
In today's fast-paced world, teachers seek continuous improvement for their courses to match their context using e-learning systems. With the aid of technologies, teachers have access to vast amount of resources which requires guidance in selecting appropriate pedagogical resources according to their context. Pedagogical resources recommender system should take into account this context to offer best recommendations dedicated to this user-teacher's context. This paper explores the impact of context-awareness in enhancing the performance of pedagogical resources recommender systems, focusing on three main approaches: contextual pre-filtering, contextual post-filtering, and contextual modeling. This paper proposes an enhancement to the context-awareness of a 2D pedagogical resources recommender system by integrating the contextual modeling approach into a collaborative-filtering recommendation technique. The evaluation of the proposed approach showed a significant improvement of recommendation accuracy compared to the contextual pre-filtering and post-filtering approaches.
Saudi Arabia's Vision 2030 emphasizes renewable energy to drive sustainable development, particularly leveraging solar power due to abundant sunshine. However, integrating solar photovoltaic (PV) systems into exis...
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This paper explores the architecture of the smart grid, emphasizing component modeling and the SGAM layers. It introduces optimized load flow equations and investigates algorithmic enhancements for improved power flow...
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This paper proposes an optimization technique for toroidal inductors utilizing a 2-step Monte Carlo tree search. The approach combines a global search utilizing analytical formulas with a local search involving three-...
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ISBN:
(纸本)9798350348958;9798350348965
This paper proposes an optimization technique for toroidal inductors utilizing a 2-step Monte Carlo tree search. The approach combines a global search utilizing analytical formulas with a local search involving three-dimensional finite element analysis. Through the proposed method, optimal core materials, sizes, number of turns, and windings are determined to maximize the efficiency and minimize the size of the inductors. The proposed method is applied to the optimization of the inductors in a 6.78MHz electric-coupling power transfer system. The proposed method found the optimal inductor with low computational cost and the system measurement confirmed that the optimized inductor improves over 1.8% of the system efficiency compared to the previously designed inductor.
Interactive Machine Learning (IML) seeks to integrate human expertise into machine learning processes. However, most existing algorithms cannot be applied to real world scenarios because their state spaces and/or acti...
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ISBN:
(纸本)9798350361087;9798350361070
Interactive Machine Learning (IML) seeks to integrate human expertise into machine learning processes. However, most existing algorithms cannot be applied to real world scenarios because their state spaces and/or action spaces are limited to discrete values. Furthermore, the interaction is limited to either a binary, good or bad, decision or the choice of which of the proposed solutions is the best. We therefore propose a novel framework based on Bayesian optimization (BO). Interactive Bayesian optimization (IBO) captures user preferences and provides an interface for users to shape the strategy by hand. Additionally, we've incorporated a new acquisition function, Preference Expected Improvement (PEI), to refine the system's efficiency using a probabilistic model of the user preferences. Our approach is geared towards ensuring that machines can benefit from human expertise, aiming for a more aligned and effective learning process. In the course of this work, we applied our method to simulations and in a real world task using a Franka Panda robot to show human-robot collaboration.
The proceedings contain 121 papers. The topics discussed include: DMobileNet: a novel MobileNet with dendritic learning for brain tumor detection;data-driven modeling and working condition prediction in process indust...
ISBN:
(纸本)9798350365221
The proceedings contain 121 papers. The topics discussed include: DMobileNet: a novel MobileNet with dendritic learning for brain tumor detection;data-driven modeling and working condition prediction in process industry production;dynamic gaussian mutation particle swarm optimization for accurate adaptive latent factor analysis;a GAN-based hybrid sampling method for transaction fraud detection;output feedback controls of a flexible wing under unknown constraint references;classification of reachable markings for automated manufacturing systems with multiple unreliable resources;a PID-incorporated second-order latent factor analysis model;an improved safe braking model of virtually coupled trains for closer tracking;and convolutional neural network with a novel attention mechanism for skin cancer recognition.
The transition to electric vehicles needs an implementation of suitable charging infrastructure, which can supply increasingly growing vehicle fleets. This requires an improvement of electric grid and energy supply as...
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